Sex Determination of Human Nails Based on Attenuated Total
Reflection Fourier Transform Infrared Spectroscopy in Forensic
Context

J 2023

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

MITU, Bilkis, Václav TROJAN a Lenka HALÁMKOVÁ

Základní údaje

Originální název

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

Autoři

MITU, Bilkis (840 Spojené státy), Václav TROJAN (203 Česká republika, domácí) a Lenka HALÁMKOVÁ (203 Česká republika)

Vydání

ACS SENSORS, UNITED STATES, AMER CHEMICAL SOC, 2023, 2379-3694

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10400 1.4 Chemical sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 8.900 v roce 2022

Organizační jednotka

Farmaceutická fakulta

DOI

http://dx.doi.org/10.3390/s23239412

UT WoS

001116755500001

Klíčová slova anglicky

ATR FT-IR spectroscopy; machine learning; artificial neural network (ANN); partial least-square discriminant analysis (PLS-DA); male; female

Štítky

rivok, ÚPL

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 7. 2024 08:44, Mgr. Daniela Černá

Anotace

V originále

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Citovat

MITU, Bilkis, Václav TROJAN a Lenka HALÁMKOVÁ. Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context. ACS SENSORS. UNITED STATES: AMER CHEMICAL SOC, 2023, roč. 23, č. 23, s. Neuvedeno, 15 s. ISSN 2379-3694. Dostupné z: https://dx.doi.org/10.3390/s23239412.

@article{2417777,
   author = {Mitu, Bilkis and Trojan, Václav and Halámková, Lenka},
   article_location = {UNITED STATES},
   article_number = {23},
   doi = {http://dx.doi.org/10.3390/s23239412},
   keywords = {ATR FT-IR spectroscopy; machine learning; artificial neural network (ANN); partial least-square discriminant analysis (PLS-DA); male; female},
   language = {eng},
   issn = {2379-3694},
   journal = {ACS SENSORS},
   title = {Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context},
   url = {https://www.mdpi.com/1424-8220/23/23/9412},
   volume = {23},
   year = {2023}
}

TY  - JOUR
ID  - 2417777
AU  - Mitu, Bilkis - Trojan, Václav - Halámková, Lenka
PY  - 2023
TI  - Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context
JF  - ACS SENSORS
VL  - 23
IS  - 23
SP  - Neuvedeno
EP  - Neuvedeno
PB  - AMER CHEMICAL SOC
SN  - 23793694
KW  - ATR FT-IR spectroscopy
KW  - machine learning
KW  - artificial neural network (ANN)
KW  - partial least-square discriminant analysis (PLS-DA)
KW  - male
KW  - female
UR  - https://www.mdpi.com/1424-8220/23/23/9412
N2  - This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.
ER  -

MITU, Bilkis, Václav TROJAN a Lenka HALÁMKOVÁ. Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context. \textit{ACS SENSORS}. UNITED STATES: AMER CHEMICAL SOC, 2023, roč.~23, č.~23, s.~Neuvedeno, 15 s. ISSN~2379-3694. Dostupné z: https://dx.doi.org/10.3390/s23239412.

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